Variability of Wind! Limited capability of demand response! Demand Response! Variability of power demand not met by renewables! Several Questions 1!
|
|
- Emil Rich
- 7 years ago
- Views:
Transcription
1 Demand Response ith Stochastic Reneables and Inertial Thermal Loads: A Study of Some Fundamental Issues in Modeling and Optimization Gaurav Sharma, Le ie and P. R. Kumar Dept. of Electrical and Computer Engineering Texas A&M University Variability of Wind Goal: Use reneable energy ind Problem: Highly variable stochastic prk.tamu@gmail.com Web: NINTH ANNUAL CARNEGIE MELLON CONFERENCE ON THE ELECTRICITY INDUSTRY CMU February 4, 24 /4 2/4 Demand Response Adjust demand to match supply Inertial thermal loads building air conditioners Air conditioner can be sitched off for a short hile ithout loss of comfort Traditionally under thermostatic control Limited capability of demand response Reneable energy may not be enough to satisfy load requirements Need to turn on air-conditioner T max T max T min T min 3/4 So there are itations to demand response 4/4 Variability of poer demand not met by reneables Several Questions Residual poer demand not met by reneables Time Prefer less variability so that operating reserve requirements are less To hat extent can demand of inertial loads be met by reneable sources? Ho does flexibility of load requirements, such as comfort level settings, influence ho much reneable poer can be used? Ho much flexibility can be extracted from thermal inertial loads for maximum utilization of variable generation such as ind? Time 5/4 6/4
2 Several Questions 2 To hat extent can operating reserve required be minimized? Ho beneficial is demand pooling? Can e come up ith quantitative ansers? Ho can demand pooling be done? What are the communication requirements? Ho much information exchange is needed beteen suppliers and consumers? Several Questions 3 What are the privacy implications? Does it require intrusive sensing? Ho distributed can the solution be? Ho tractable (computational complexity) is the solution? Ho robust is the solution? Ho implementable is it? Role of model features, cost functions, stochasticity assumptions, convexity, asymptotics, etc 7/4 8/4 Load Aggregator Load service entity a.k.a. Load aggregator Acts as coordinator Features of problem Possibly monitors temperature of A/Cs Possibly controls poer to cool A/Cs Appears as aggregated load 9/4 /4 Wind model Temperature dynamics Model ind as a finite state Markov process Wind poer Time x i (t) Temperature of load i Poer given to load i Time Even simpler model for illustration On-Off process W Wind poer Time ON OFF /4 Inertial thermal load (A/C) dynamics ẋ i (t) =h i (t) P i (t)» Rate of change in temperature = Ambient heating Poer for cooling. 2/4
3 User specified comfort range Range of comfortable temperature [, ] Either: Enforce hard constraint Or: Penalize the violations t t Allo but penalize the violation 3/4 Stochastic control problem: Comfort violation probability Minimize the probability of leaving a user specified comfort range [, ] Wind process Temperature dynamics Cost function P i (t) Markov process T T Z T ẋ i (t) =h i I(x i (t) > )dt i P i (t) 4/4 Optimal policy in comfort violation probability model Temperature Wind poer Load 2 Load Time Theorem: Provide poer to the coolest load that is above the temperature range. Issue: Unfair, temperatures of some loads ill remain higher than others Possible solution: Minimize the variance of comfort violation 5/4 Stochastic control problem: Variance minimization Stochastic control problem: Z T Cost function Theorem: Optimal policy synchronizes loads Load 2 Temperature max Load min Wind poer T T [(x i (t) i ) + ] 2 dt Loads ill remain synchronized after this time instant Time 6/4 Requirement for reserves (of nonreneable poer) Temperatures can go very high occasionally Cost function for reducing operating reserves Desire lo operating reserve requirements Temperature Load 2 Load More variability Less variability Prefer this Hard constraints require reliable non-reneable source Use non-reneable poe Need to turn on air-conditioner, maintain constraint but no ind available 7/4 t Impose a quadratic cost on non-reneable poer usage Z ( Pi n (t)) 2 dt t 8/4
4 Stochastic control problem: Reduction of variability ith temperature constraint Stochastic control Problem: Wind process P i (t) Markov process Temperature dynamics ẋ i (t) =h i Pi (t) Non-reneable poer P n i (t) P n i (t) Temperature constraint x i (t) 2 [, ], 8i Z T Quadratic cost to reduce variability [ T T i P n i (t)] 2 dt 9/4 Theorem: Optimal policy still synchronizes loads Optimal solution: Reduction of variability ith temperature constraint Loads ill remain synchronized after this time instant Temperature Load 2 Counter-intuitive?? Load Question: Is there some modification in the model or cost function hich leads to de-synchronization? 2/4 Ho to induce desynchronization: Markov model for changes in Θ max Stochastic control problem: Stochastic variation of temperature constraints Suppose users occasionally change setting at the same time E.g. Super Bol game time. Wind process: P i (t) Markov process Temperature dynamics: ẋ i (t) =h i Pi (t) Pi n (t) E.g. 2 max max is a to state Markov process Comfort range t 2 max / h / l max 2/4 Non-reneable poer Pi n (t) Stochastic comfort level (t) Markov process, (t) 2 { max, 2 max} Temperature constraint: x i (t) 2 [, 2 max], 8i Maximum cooling rate: Pi n (t) =M If x i(t) > (t) Z Quadratic cost: T [ Pi n (t)] 2 dt T T i 22/4 Optimal de-synchronization and resynchronization It is optimal to break symmetry at high temperatures Hedges against the future eventuality that the thermostats are sitched lo Vector field of optimal solution Nature of optimal solution De-synchronization at high temperatures Re-synchronization at lo temperatures Temperature 2 max max Temperature load 2 min Time Temperature load 23/4 Vector field of temperature changes 24/4
5 July 3, 2, P. R. Kumar July 3, 2, P. R. Kumar Local concavity/convexity of optimal cost-to-go resulting from HJB equation A heuristic Approximate optimal policy Keep the temperatures apart Locally concave De-synchronization above temperature 9» Provide poer to load ith minimum temperature amongst all loads ith temperature higher than 2» Bring the temperatures together for loads in[ min, 2 ] Cost to go Bring the temperatures together 4 3 Poer is assumed affine in [ min, 2 ] and [ 2, max ] Policy is a function of a fe parameters, optimize iteratively Load 2 To loads optimal solution along x = x Load Non reneable poer min But optimal policy is difficult to compute 25/4 2 2max Temperature But requires intrusive sensing 26/4 July 3, 2, P. R. Kumar July 3, 2, P. R. Kumar Thermostatic control ith set points Z A possibly implementable architecture of a solution Load service entity - Senses ind ddpoer - Sets set ddpoints Zi Z2 ZN 27/4 28/4 July 3, 2, P. R. Kumar Control policy July 3, 2, P. R. Kumar Information flo in architecture Information from LSE to consumer Minimal information needed to be responsive? Wind not bloing Zi Wind bloing or not = Price signal LSE need not set thermostat set-points Only needs to set empirical distribution of set-points Not detailed actuation Ambient temperature rise t Cooling using ind No flo of state information from home to LSE Information and communication requirements Price signal to consumers Infrequent distribution signaling to consumers Cooling using non-reneable 29/4 (LSE also monitors total poer usage by consumers) 3/4
6 Overall optimization problem Stochastic Wind process: Temperature dynamics: Pi W t Overall optimization problem Stochastic Wind process: Temperature dynamics: Pi W t Comfort setting dynamics: 2 Cost: Min 4 T T Z T 2 P g i (t) + 3 [(x i (t) M i (t)) + ] 2 dt5 2 Cost: Min 4 T T Z T 2 P g i (t) + 3 [(x i (t) M i (t)) + ] 2 dt5 Grid poer variation cost User discomfort cost Grid poer variation cost User discomfort cost 3/4 32/4 Overall optimization problem Ho to choose {Z so as to minimize: 2,Z 2,...,Z N } Z 2 3 Min 4 T P g i T T (t) + [(x i (t) M i (t)) + ] 2 dt5 Difficult: Complex as N is large, high dimensional. Need to solve different problem for different N Solution: Study asymptotic it as N. Solution becomes explicit And asymptotic solution is also nearly optimal even for small N 33/4 Continuum it of Z-policy Continuum of loads in [,] u(x)= fraction of loads ith set-points less than x, = empirical distribution of set-points Cost function C [,] (u) = Z 2 Masses at set points Z 2 (z)u (z)dz +(h) 2 ( 2 + u 2 (z Z 2 (h) 2 ( 2 + u 2 (z)p({ z = z} \ { z+dz <z+ dz})) 34/4 Continuum it optimization problem Resulting variational problem: Solution to variational problem (Euler-Lagrange): Optimal solution of continuum it =) 2(h)2 u(x)d(x) d dx (x) =) u(x) = (x) 2(h) 2 D(x) 2 Z Not so fast, singularity: Solution is given by: u (z) 2 F ( min, (z) 2(h) 2 D(z) If z< 2 If z = 2. 35/4 Optimal desynchronization of demand response 36/4
7 Z-policy: Finite population approximation from continuum it asymptotic Some simulation results Generate {Z i } N it to approximate continuum This appears to ork very ell even hen N is small Even N = 5 Distribution Optimal infinite case distribution Approximation based finite distribution Optimal finite distribution Nonreneable cost Variation cost.8 Optimal grid cost.6 Grid cost for generated Z Optimal temperature variation cost Time.8 Variation cost for generated Z x Setpoint, Z Total cost Optimal total cost Time x 4.6 Total cost for generated Z / Time x 4 38/4 Concluding remarks Attempt to develop an architecture and tractable solution for demand response Many extensions needed and feasible Response to comfort variations Availability of ind poer Generalize ind model, temperature dynamics, etc. Thank you 39/4 4/4
Communication and Embedded Systems: Towards a Smart Grid. Radu Stoleru, Alex Sprintson, Narasimha Reddy, and P. R. Kumar
Communication and Embedded Systems: Towards a Smart Grid Radu Stoleru, Alex Sprintson, Narasimha Reddy, and P. R. Kumar Alex Sprintson Smart grid communication Key enabling technology Collecting data Control
More informationCRITERIUM FOR FUNCTION DEFININING OF FINAL TIME SHARING OF THE BASIC CLARK S FLOW PRECEDENCE DIAGRAMMING (PDM) STRUCTURE
st Logistics International Conference Belgrade, Serbia 8-30 November 03 CRITERIUM FOR FUNCTION DEFININING OF FINAL TIME SHARING OF THE BASIC CLARK S FLOW PRECEDENCE DIAGRAMMING (PDM STRUCTURE Branko Davidović
More informationA Quantitative Approach to the Performance of Internet Telephony to E-business Sites
A Quantitative Approach to the Performance of Internet Telephony to E-business Sites Prathiusha Chinnusamy TransSolutions Fort Worth, TX 76155, USA Natarajan Gautam Harold and Inge Marcus Department of
More informationREDUCING RISK OF HAND-ARM VIBRATION INJURY FROM HAND-HELD POWER TOOLS INTRODUCTION
Health and Safety Executive Information Document HSE 246/31 REDUCING RISK OF HAND-ARM VIBRATION INJURY FROM HAND-HELD POWER TOOLS INTRODUCTION 1 This document contains internal guidance hich has been made
More informationSensitivity analysis of utility based prices and risk-tolerance wealth processes
Sensitivity analysis of utility based prices and risk-tolerance wealth processes Dmitry Kramkov, Carnegie Mellon University Based on a paper with Mihai Sirbu from Columbia University Math Finance Seminar,
More informationLabor Demand. 1. The Derivation of the Labor Demand Curve in the Short Run:
CON 361: Labor conomics 1. The Derivation o the Curve in the Short Run: We ill no complete our discussion o the components o a labor market by considering a irm s choice o labor demand, beore e consider
More informationWorld Service Office PO Box 9999 Van Nuys, CA 91409 USA. World Service Office Europe 48 Rue de l Eté B-1050 Brussels, Belgium
means regularly attending N meetings and events, connecting ith other members over the phone, and spending time ith other recovering addicts ho that their parents feel more comfortable ith their involvement
More informationLCs for Binary Classification
Linear Classifiers A linear classifier is a classifier such that classification is performed by a dot product beteen the to vectors representing the document and the category, respectively. Therefore it
More informationEnabling 24/7 Automated Demand Response and the Smart Grid using Dynamic Forward Price Offers
Enabling 24/7 Automated Demand Response and the Smart Grid using Dynamic Forward Price Offers Presented to ISO/RTO Council by Edward G. Cazalet, PhD The Cazalet Group ed@cazalet.com www.cazalet.com 650-949-0560
More information12. Inner Product Spaces
1. Inner roduct Spaces 1.1. Vector spaces A real vector space is a set of objects that you can do to things ith: you can add to of them together to get another such object, and you can multiply one of
More informationWho Should Sell Stocks?
Who Should Sell Stocks? Ren Liu joint work with Paolo Guasoni and Johannes Muhle-Karbe ETH Zürich Imperial-ETH Workshop on Mathematical Finance 2015 1 / 24 Merton s Problem (1969) Frictionless market consisting
More informationPricing and calibration in local volatility models via fast quantization
Pricing and calibration in local volatility models via fast quantization Parma, 29 th January 2015. Joint work with Giorgia Callegaro and Martino Grasselli Quantization: a brief history Birth: back to
More informationDesigning a learning system
Lecture Designing a learning system Milos Hauskrecht milos@cs.pitt.edu 539 Sennott Square, x4-8845 http://.cs.pitt.edu/~milos/courses/cs750/ Design of a learning system (first vie) Application or Testing
More informationEXIT TIME PROBLEMS AND ESCAPE FROM A POTENTIAL WELL
EXIT TIME PROBLEMS AND ESCAPE FROM A POTENTIAL WELL Exit Time problems and Escape from a Potential Well Escape From a Potential Well There are many systems in physics, chemistry and biology that exist
More informationDynamic Thermal Ratings in Real-time Dispatch Market Systems
Dynamic Thermal Ratings in Real-time Dispatch Market Systems Kwok W. Cheung, Jun Wu GE Grid Solutions Imagination at work. FERC 2016 Technical Conference June 27-29, 2016 Washington DC 1 Outline Introduction
More informationBig Data - Lecture 1 Optimization reminders
Big Data - Lecture 1 Optimization reminders S. Gadat Toulouse, Octobre 2014 Big Data - Lecture 1 Optimization reminders S. Gadat Toulouse, Octobre 2014 Schedule Introduction Major issues Examples Mathematics
More informationCHAPTER 1 INTRODUCTION
CHAPTER 1 INTRODUCTION Power systems form the largest man made complex system. It basically consists of generating sources, transmission network and distribution centers. Secure and economic operation
More informationNeuro-Dynamic Programming An Overview
1 Neuro-Dynamic Programming An Overview Dimitri Bertsekas Dept. of Electrical Engineering and Computer Science M.I.T. September 2006 2 BELLMAN AND THE DUAL CURSES Dynamic Programming (DP) is very broadly
More informationStochastic Gradient Method: Applications
Stochastic Gradient Method: Applications February 03, 2015 P. Carpentier Master MMMEF Cours MNOS 2014-2015 114 / 267 Lecture Outline 1 Two Elementary Exercices on the Stochastic Gradient Two-Stage Recourse
More informationOPTIMIZING WEB SERVER'S DATA TRANSFER WITH HOTLINKS
OPTIMIZING WEB SERVER'S DATA TRANSFER WIT OTLINKS Evangelos Kranakis School of Computer Science, Carleton University Ottaa,ON. K1S 5B6 Canada kranakis@scs.carleton.ca Danny Krizanc Department of Mathematics,
More informationSOLUTION PAPER GET THERE FIRST
GET THERE FIRST Keep Your Organization Out in Front Opportunities for groth are out there. Businesses need connectivity to thrive and the economic recovery is providing significant indos of groth based
More informationOptimal shift scheduling with a global service level constraint
Optimal shift scheduling with a global service level constraint Ger Koole & Erik van der Sluis Vrije Universiteit Division of Mathematics and Computer Science De Boelelaan 1081a, 1081 HV Amsterdam The
More informationCommand Alkon. 7Dispatch Optimizati n
Command Alkon 7Dispatch Optimizati n BOOKLETS 1 Corporate Overvie 2 Ready-Mixed Concrete COMMANDconcrete 3 Bulk Materials & Automation 4 Concrete Plant Automation 5 Quality Control 6 Business Integration
More informationLyapunov Stability Analysis of Energy Constraint for Intelligent Home Energy Management System
JAIST Reposi https://dspace.j Title Lyapunov stability analysis for intelligent home energy of energ manageme Author(s)Umer, Saher; Tan, Yasuo; Lim, Azman Citation IEICE Technical Report on Ubiquitous
More informationMeters to Models: Using Smart Meter Data to Predict and Control Home Energy Use
Meters to Models: Using Smart Meter Data to Predict and Control Home Energy Use Krystian X. Perez a,*, Dr. Michael Baldea a,b and Dr. Thomas F. Edgar a,c a McKetta Department of Chemical Engineering, The
More informationindoor air quality and energy saving TECHNICAL DATA HRE-TOP EC VENTILATION UNIT WITH HEAT RECOVERY FOR COMMERCIAL AND INDUSTRIAL BUILDINGS
indoor air quality and energy saving TECHNICAL DATA HRE-TOP EC VENTILATION UNIT WITH HEAT RECOVERY FOR COMMERCIAL AND INDUSTRIAL BUILDINGS HRETOP-EC Non-residential ventilator unit ith dual flo and high
More informationOn the mathematical theory of splitting and Russian roulette
On the mathematical theory of splitting and Russian roulette techniques St.Petersburg State University, Russia 1. Introduction Splitting is an universal and potentially very powerful technique for increasing
More informationStochastic control of HVAC systems: a learning-based approach. Damiano Varagnolo
Stochastic control of HVAC systems: a learning-based approach Damiano Varagnolo Something about me 2 Something about me Post-Doc at KTH Post-Doc at U. Padova Visiting Scholar at UC Berkeley Ph.D. Student
More informationScheduling agents using forecast call arrivals at Hydro-Québec s call centers
Scheduling agents using forecast call arrivals at Hydro-Québec s call centers Marie Pelleau 1, Louis-Martin Rousseau 2, Pierre L Ecuyer 1, Walid Zegal 3, Louis Delorme 3 1 Université de Montréal, Montreal,
More informationComputing the Electricity Market Equilibrium: Uses of market equilibrium models
Computing the Electricity Market Equilibrium: Uses of market equilibrium models Ross Baldick Department of Electrical and Computer Engineering The University of Texas at Austin April 2007 Abstract We discuss
More informationExpert Systems with Applications
Expert Systems ith Applications 36 (2009) 5450 5455 Contents lists available at ScienceDirect Expert Systems ith Applications journal homepage:.elsevier.com/locate/esa Trading rule discovery in the US
More informationLecture 3: Linear methods for classification
Lecture 3: Linear methods for classification Rafael A. Irizarry and Hector Corrada Bravo February, 2010 Today we describe four specific algorithms useful for classification problems: linear regression,
More informationDecentralization and Private Information with Mutual Organizations
Decentralization and Private Information with Mutual Organizations Edward C. Prescott and Adam Blandin Arizona State University 09 April 2014 1 Motivation Invisible hand works in standard environments
More informationTwo-Stage Stochastic Linear Programs
Two-Stage Stochastic Linear Programs Operations Research Anthony Papavasiliou 1 / 27 Two-Stage Stochastic Linear Programs 1 Short Reviews Probability Spaces and Random Variables Convex Analysis 2 Deterministic
More informationQuasi-static evolution and congested transport
Quasi-static evolution and congested transport Inwon Kim Joint with Damon Alexander, Katy Craig and Yao Yao UCLA, UW Madison Hard congestion in crowd motion The following crowd motion model is proposed
More informationError Bound for Classes of Polynomial Systems and its Applications: A Variational Analysis Approach
Outline Error Bound for Classes of Polynomial Systems and its Applications: A Variational Analysis Approach The University of New South Wales SPOM 2013 Joint work with V. Jeyakumar, B.S. Mordukhovich and
More informationProtocol for the Certification of Energy Simulation Software: First edition, December 2009
Copyright Agrément South Africa, December 2009 The master copy of this document appears on the website: http://www.agrement.co.za Protocol for the Certification of Energy Simulation Software: First edition,
More informationLeveraging Multipath Routing and Traffic Grooming for an Efficient Load Balancing in Optical Networks
Leveraging ultipath Routing and Traffic Grooming for an Efficient Load Balancing in Optical Netorks Juliana de Santi, André C. Drummond* and Nelson L. S. da Fonseca University of Campinas, Brazil Email:
More informationMoral Hazard. Itay Goldstein. Wharton School, University of Pennsylvania
Moral Hazard Itay Goldstein Wharton School, University of Pennsylvania 1 Principal-Agent Problem Basic problem in corporate finance: separation of ownership and control: o The owners of the firm are typically
More informationUser Manual IC-485AI 2002-09-27
User Manual IC-485AI Note: This equipment has been tested and found to comply ith the limits for a Class A digital device pursuant to Part 15 of the FCC Rules. These limits are designed to provide reasonable
More informationThe Research on Demand Forecasting of Supply Chain Based on ICCELMAN
505 A publication of CHEMICAL ENGINEERING TRANSACTIONS VOL. 46, 2015 Guest Editors: Peiyu Ren, Yancang Li, Huiping Song Copyright 2015, AIDIC Servizi S.r.l., ISBN 978-88-95608-37-2; ISSN 2283-9216 The
More information1 Norms and Vector Spaces
008.10.07.01 1 Norms and Vector Spaces Suppose we have a complex vector space V. A norm is a function f : V R which satisfies (i) f(x) 0 for all x V (ii) f(x + y) f(x) + f(y) for all x,y V (iii) f(λx)
More informationLecture notes: single-agent dynamics 1
Lecture notes: single-agent dynamics 1 Single-agent dynamic optimization models In these lecture notes we consider specification and estimation of dynamic optimization models. Focus on single-agent models.
More informationRAJALAKSHMI ENGINEERING COLLEGE MA 2161 UNIT I - ORDINARY DIFFERENTIAL EQUATIONS PART A
RAJALAKSHMI ENGINEERING COLLEGE MA 26 UNIT I - ORDINARY DIFFERENTIAL EQUATIONS. Solve (D 2 + D 2)y = 0. 2. Solve (D 2 + 6D + 9)y = 0. PART A 3. Solve (D 4 + 4)x = 0 where D = d dt 4. Find Particular Integral:
More informationMODELLING AND OPTIMIZATION OF DIRECT EXPANSION AIR CONDITIONING SYSTEM FOR COMMERCIAL BUILDING ENERGY SAVING
MODELLING AND OPTIMIZATION OF DIRECT EXPANSION AIR CONDITIONING SYSTEM FOR COMMERCIAL BUILDING ENERGY SAVING V. Vakiloroaya*, J.G. Zhu, and Q.P. Ha School of Electrical, Mechanical and Mechatronic Systems,
More information-Todd Wittman, IMA Industrial Problems Seminar, 10/29/04
Decreasing Blur and Increasing Resolution in Barcode Scanning What is a Barcode? A barcode encodes information in the relative idths of the bars. Todd Wittman IMA Industrial Problems Seminar October 9,
More informationDual Methods for Total Variation-Based Image Restoration
Dual Methods for Total Variation-Based Image Restoration Jamylle Carter Institute for Mathematics and its Applications University of Minnesota, Twin Cities Ph.D. (Mathematics), UCLA, 2001 Advisor: Tony
More informationVectors Math 122 Calculus III D Joyce, Fall 2012
Vectors Math 122 Calculus III D Joyce, Fall 2012 Vectors in the plane R 2. A vector v can be interpreted as an arro in the plane R 2 ith a certain length and a certain direction. The same vector can be
More information2. Information Economics
2. Information Economics In General Equilibrium Theory all agents had full information regarding any variable of interest (prices, commodities, state of nature, cost function, preferences, etc.) In many
More informationThe Heat Equation. Lectures INF2320 p. 1/88
The Heat Equation Lectures INF232 p. 1/88 Lectures INF232 p. 2/88 The Heat Equation We study the heat equation: u t = u xx for x (,1), t >, (1) u(,t) = u(1,t) = for t >, (2) u(x,) = f(x) for x (,1), (3)
More information2.1 The performance of bus air conditioning system is evaluated in the following operating conditions
1.0 Scope:- This test procedure is applicable for testing the Air Conditioning system performance at hot ambient and very hot ambient condition on intracity urban bus in a climatic hot chamber. This procedure
More informationTwo-Settlement Electric Power Markets with Dynamic-Price Contracts
1 Two-Settlement Electric Power Markets with Dynamic-Price Contracts Huan Zhao, Auswin Thomas, Pedram Jahangiri, Chengrui Cai, Leigh Tesfatsion, and Dionysios Aliprantis 27 July 2011 IEEE PES GM, Detroit,
More informationIntroduction to General and Generalized Linear Models
Introduction to General and Generalized Linear Models General Linear Models - part I Henrik Madsen Poul Thyregod Informatics and Mathematical Modelling Technical University of Denmark DK-2800 Kgs. Lyngby
More informationWhat is Modeling and Simulation and Software Engineering?
What is Modeling and Simulation and Software Engineering? V. Sundararajan Scientific and Engineering Computing Group Centre for Development of Advanced Computing Pune 411 007 vsundar@cdac.in Definitions
More informationOnline Appendix I: A Model of Household Bargaining with Violence. In this appendix I develop a simple model of household bargaining that
Online Appendix I: A Model of Household Bargaining ith Violence In this appendix I develop a siple odel of household bargaining that incorporates violence and shos under hat assuptions an increase in oen
More informationHow To Control Humidity With A Humidifier
Control of ventilation and air conditioning plants Building Technologies s Contents.Temperature control in air treatment systems 2. Humidity controls 3. Recirculated air mixing. Internal heat sources 6.2
More informationApplication of Building Energy Simulation to Air-conditioning Design
Hui, S. C. M. and K. P. Cheung, 1998. Application of building energy simulation to air-conditioning design, In Proc. of the Mainland-Hong Kong HVAC Seminar '98, 23-25 March 1998, Beijing, pp. 12-20. (in
More informationFamily-Teacher Partnerships MPC-45
Family-Teacher Partnerships MPC-45 Parents and Teachers as Partners The education of all children is a shared social responsibility in hich parents and educators play critical roles. Effective parent-teacher
More informationTetris: A Study of Randomized Constraint Sampling
Tetris: A Study of Randomized Constraint Sampling Vivek F. Farias and Benjamin Van Roy Stanford University 1 Introduction Randomized constraint sampling has recently been proposed as an approach for approximating
More informationVENDOR MANAGED INVENTORY
VENDOR MANAGED INVENTORY Martin Savelsbergh School of Industrial and Systems Engineering Georgia Institute of Technology Joint work with Ann Campbell, Anton Kleywegt, and Vijay Nori Distribution Systems:
More information1 Teaching notes on GMM 1.
Bent E. Sørensen January 23, 2007 1 Teaching notes on GMM 1. Generalized Method of Moment (GMM) estimation is one of two developments in econometrics in the 80ies that revolutionized empirical work in
More informationEstimating the Degree of Activity of jumps in High Frequency Financial Data. joint with Yacine Aït-Sahalia
Estimating the Degree of Activity of jumps in High Frequency Financial Data joint with Yacine Aït-Sahalia Aim and setting An underlying process X = (X t ) t 0, observed at equally spaced discrete times
More informationProbability and Random Variables. Generation of random variables (r.v.)
Probability and Random Variables Method for generating random variables with a specified probability distribution function. Gaussian And Markov Processes Characterization of Stationary Random Process Linearly
More informationAffine-structure models and the pricing of energy commodity derivatives
Affine-structure models and the pricing of energy commodity derivatives Nikos K Nomikos n.nomikos@city.ac.uk Cass Business School, City University London Joint work with: Ioannis Kyriakou, Panos Pouliasis
More informationInwall Room Temperature Unit
Inwall Room Temperature Unit TM11B01KNX TM11B11KNX TM11B21KNX Product Handbook Product: Inwall Room Temperature Unit Order Code: TM11B01KNX TM11B11KNX TM11B21KNX Application Program ETS: TM11B_1KNX Inwall
More informationEnergy Efficient Cloud Computing: Challenges and Solutions
Energy Efficient Cloud Computing: Challenges and Solutions Burak Kantarci and Hussein T. Mouftah School of Electrical Engineering and Computer Science University of Ottawa Ottawa, ON, Canada 08 September
More informationHeat Transfer Prof. Dr. Aloke Kumar Ghosal Department of Chemical Engineering Indian Institute of Technology, Guwahati
Heat Transfer Prof. Dr. Aloke Kumar Ghosal Department of Chemical Engineering Indian Institute of Technology, Guwahati Module No. # 02 One Dimensional Steady State Heat Transfer Lecture No. # 05 Extended
More informationA simple analysis of the TV game WHO WANTS TO BE A MILLIONAIRE? R
A simple analysis of the TV game WHO WANTS TO BE A MILLIONAIRE? R Federico Perea Justo Puerto MaMaEuSch Management Mathematics for European Schools 94342 - CP - 1-2001 - DE - COMENIUS - C21 University
More informationTexPoint fonts used in EMF. Read the TexPoint manual before you delete this box.: AAAAA
2015 School of Information Technology and Electrical Engineering at the University of Queensland TexPoint fonts used in EMF. Read the TexPoint manual before you delete this box.: AAAAA Schedule Week Date
More informationPricing Transmission
1 / 37 Pricing Transmission Quantitative Energy Economics Anthony Papavasiliou 2 / 37 Pricing Transmission 1 Equilibrium Model of Power Flow Locational Marginal Pricing Congestion Rent and Congestion Cost
More informationCheap Talk : Multiple Senders and Multiple Receivers
Corso di Laurea in Economia indirizzo Models and Methods of Quantitative Economics Prova finale di Laurea Cheap Talk : Multiple Senders and Multiple Receivers Relatore Prof. Piero Gottardi Laureando Arya
More informationLectures on Stochastic Processes. William G. Faris
Lectures on Stochastic Processes William G. Faris November 8, 2001 2 Contents 1 Random walk 7 1.1 Symmetric simple random walk................... 7 1.2 Simple random walk......................... 9 1.3
More informationInstitut für angewandtes Stoffstrommanagement (IfaS) 16.03.2010. Competive Innovation
Environmental Management Systems - Introduction - Jackeline Martínez Gómez Environmental Engineer MSc. In International Material Flo Management PhD- Candidat (TU Dresden) Summer School Curitiba, 15.03
More informationTask 2.2.11 CMU Report 06: Programs for Design Analysis Support and Simulation Integration. Department of Energy Award # EE0004261
Task 2.2.11 CMU Report 06: Programs for Design Analysis Support and Simulation Integration Department of Energy Award # EE0004261 Omer T. Karaguzel, PhD Candidate Khee Poh Lam, PhD, RIBA, Professor Of
More informationService Performance Analysis and Improvement for a Ticket Queue with Balking Customers. Long Gao. joint work with Jihong Ou and Susan Xu
Service Performance Analysis and Improvement for a Ticket Queue with Balking Customers joint work with Jihong Ou and Susan Xu THE PENNSYLVANIA STATE UNIVERSITY MSOM, Atlanta June 20, 2006 Outine Introduction
More informationEquilibrium computation: Part 1
Equilibrium computation: Part 1 Nicola Gatti 1 Troels Bjerre Sorensen 2 1 Politecnico di Milano, Italy 2 Duke University, USA Nicola Gatti and Troels Bjerre Sørensen ( Politecnico di Milano, Italy, Equilibrium
More informationChapter 14. Three-by-Three Matrices and Determinants. A 3 3 matrix looks like a 11 a 12 a 13 A = a 21 a 22 a 23
1 Chapter 14. Three-by-Three Matrices and Determinants A 3 3 matrix looks like a 11 a 12 a 13 A = a 21 a 22 a 23 = [a ij ] a 31 a 32 a 33 The nmber a ij is the entry in ro i and colmn j of A. Note that
More informationCreating a NL Texas Hold em Bot
Creating a NL Texas Hold em Bot Introduction Poker is an easy game to learn by very tough to master. One of the things that is hard to do is controlling emotions. Due to frustration, many have made the
More informationSpatial Decomposition/Coordination Methods for Stochastic Optimal Control Problems. Practical aspects and theoretical questions
Spatial Decomposition/Coordination Methods for Stochastic Optimal Control Problems Practical aspects and theoretical questions P. Carpentier, J-Ph. Chancelier, M. De Lara, V. Leclère École des Ponts ParisTech
More information14.451 Lecture Notes 10
14.451 Lecture Notes 1 Guido Lorenzoni Fall 29 1 Continuous time: nite horizon Time goes from to T. Instantaneous payo : f (t; x (t) ; y (t)) ; (the time dependence includes discounting), where x (t) 2
More informationTD(0) Leads to Better Policies than Approximate Value Iteration
TD(0) Leads to Better Policies than Approximate Value Iteration Benjamin Van Roy Management Science and Engineering and Electrical Engineering Stanford University Stanford, CA 94305 bvr@stanford.edu Abstract
More informationA New Quantitative Behavioral Model for Financial Prediction
2011 3rd International Conference on Information and Financial Engineering IPEDR vol.12 (2011) (2011) IACSIT Press, Singapore A New Quantitative Behavioral Model for Financial Prediction Thimmaraya Ramesh
More informationINDIRECT INFERENCE (prepared for: The New Palgrave Dictionary of Economics, Second Edition)
INDIRECT INFERENCE (prepared for: The New Palgrave Dictionary of Economics, Second Edition) Abstract Indirect inference is a simulation-based method for estimating the parameters of economic models. Its
More informationStochastic Models for Inventory Management at Service Facilities
Stochastic Models for Inventory Management at Service Facilities O. Berman, E. Kim Presented by F. Zoghalchi University of Toronto Rotman School of Management Dec, 2012 Agenda 1 Problem description Deterministic
More informationNIST Calibration Services for Liquid Volume NIST Special Publication 250-72
NIST Calibration Services for Liquid olume NIST Special Publication 250-72 ern E. Bean, Pedro I. Espina, John. D. Wright, John F. Houser, Sherry D. Sheckels, and Aaron N. Johnson March 24, 2006 Fluid Metrology
More informationEnergy Management and Profit Maximization of Green Data Centers
Energy Management and Profit Maximization of Green Data Centers Seyed Mahdi Ghamkhari, B.S. A Thesis Submitted to Graduate Faculty of Texas Tech University in Partial Fulfilment of the Requirements for
More informationProtocol for the Certification of Energy Simulation Software: Second edition, September 2011
Copyright Agrément South Africa, September 2011 The master copy of this document appears on the website: http://www.agrement.co.za SANS 10400: The application of the National Building Regulations: Protocol
More informationMODELLING COUPLED HEAT AND AIR FLOW: PING-PONG VS ONIONS
MODELLING COUPLED HEAT AND AIR FLOW: PING-PONG VS ONIONS Jan Hensen University of Strathclyde Energy Systems Research Unit 75 Montrose Street, GLASGOW G1 1XJ, Scotland Email: jan@esru.strath.ac.uk SYNOPSIS
More informationScheduling Policies, Batch Sizes, and Manufacturing Lead Times
To appear in IIE Transactions Scheduling Policies, Batch Sizes, and Manufacturing Lead Times Saifallah Benjaafar and Mehdi Sheikhzadeh Department of Mechanical Engineering University of Minnesota Minneapolis,
More informationApproximation Algorithms
Approximation Algorithms or: How I Learned to Stop Worrying and Deal with NP-Completeness Ong Jit Sheng, Jonathan (A0073924B) March, 2012 Overview Key Results (I) General techniques: Greedy algorithms
More informationIdentification algorithms for hybrid systems
Identification algorithms for hybrid systems Giancarlo Ferrari-Trecate Modeling paradigms Chemistry White box Thermodynamics System Mechanics... Drawbacks: Parameter values of components must be known
More informationLecture 5/6. Enrico Bini
Advanced Lecture 5/6 November 8, 212 Outline 1 2 3 Theorem (Lemma 3 in [?]) Space of feasible C i N is EDF-schedulable if and only if i U i 1 and: t D n i=1 { max, t + Ti D i T i } C i t with D = {d i,k
More informationWhite Paper Nest Learning Thermostat Efficiency Simulation for France. Nest Labs September 2014
White Paper Nest Learning Thermostat Efficiency Simulation for France Nest Labs September 2014 Introduction This white paper gives an overview of potential energy savings using the Nest Learning Thermostat
More information24. The Branch and Bound Method
24. The Branch and Bound Method It has serious practical consequences if it is known that a combinatorial problem is NP-complete. Then one can conclude according to the present state of science that no
More information440 Geophysics: Heat flow with finite differences
440 Geophysics: Heat flow with finite differences Thorsten Becker, University of Southern California, 03/2005 Some physical problems, such as heat flow, can be tricky or impossible to solve analytically
More informationProteinPilot Report for ProteinPilot Software
ProteinPilot Report for ProteinPilot Software Detailed Analysis of Protein Identification / Quantitation Results Automatically Sean L Seymour, Christie Hunter SCIEX, USA Pow erful mass spectrometers like
More informationModeling and performance analysis of a house heating system with a ground coupled heat pump
Energy Education Science and Technology Part A: Energy Science and Research 1 Volume (issues) 8(): 669-68 Modeling and performance analysis of a house heating system ith a ground coupled heat pump Recep
More informationFLUX / GOT-It Finite Element Analysis of electromagnetic devices Maccon GmbH
FLUX / GOT-It Finite Element Analysis of electromagnetic devices Maccon GmbH Entwurfswerkzeuge für elektrische Maschinen MACCON GmbH 09/04/2013 1 Flux software Modeling electromagnetic and thermal phenomena
More informationA Network Flow Approach in Cloud Computing
1 A Network Flow Approach in Cloud Computing Soheil Feizi, Amy Zhang, Muriel Médard RLE at MIT Abstract In this paper, by using network flow principles, we propose algorithms to address various challenges
More information